{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import random\n",
    "import cPickle as pickle\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\",100)\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "\n",
    "np.random.seed(1337)\n",
    "import keras\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense\n",
    "from keras.layers.core import Dropout\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.convolutional import Convolution1D, MaxPooling1D\n",
    "from keras.constraints import maxnorm\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "### Plotting settings ###\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.labelpad'] = 5\n",
    "plt.rcParams['axes.linewidth']= 2.5\n",
    "plt.rcParams['xtick.labelsize']= 24\n",
    "plt.rcParams['ytick.labelsize']= 24\n",
    "plt.rcParams['axes.labelsize'] = 20\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "plt.rcParams['lines.linewidth'] = 7\n",
    "plt.rcParams['legend.markerscale'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 20\n",
    "plt.rcParams['xtick.major.size'] = 12\n",
    "plt.rcParams['ytick.major.size'] = 12\n",
    "plt.rcParams['xtick.minor.size'] = 8\n",
    "plt.rcParams['ytick.minor.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 3\n",
    "plt.rcParams['ytick.minor.width'] = 3\n",
    "plt.rcParams['xtick.major.width'] = 4\n",
    "plt.rcParams['ytick.major.width'] = 4\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "plt.rcParams['figure.autolayout'] = True\n",
    "plt.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams['xtick.color'] = 'black'\n",
    "plt.rcParams['ytick.color'] = 'black'\n",
    "plt.rcParams['axes.labelcolor'] = \"black\"\n",
    "plt.rcParams['axes.edgecolor'] = 'black'\n",
    "\n",
    "def train_model(x, y, border_mode='same', inp_len=50, nodes=40, layers=3, filter_len=8, nbr_filters=120,\n",
    "                dropout1=0, dropout2=0, dropout3=0, nb_epoch=3):\n",
    "    ''' Build model archicture and fit.'''\n",
    "    model = Sequential()\n",
    "    if layers >= 1:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "    if layers >= 2:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "        model.add(Dropout(dropout1))\n",
    "    if layers >= 3:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "        model.add(Dropout(dropout2))\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(nodes))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(dropout3))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    #compile the model\n",
    "    adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "    model.compile(loss='mean_squared_error', optimizer=adam)\n",
    "\n",
    "    model.fit(x, y, batch_size=128, nb_epoch=nb_epoch, verbose=1)\n",
    "    return model\n",
    "\n",
    "def test_data(df, model, test_seq, obs_col, output_col='pred'):\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    scaler.fit(df[obs_col].reshape(-1,1))\n",
    "    #df.loc[:,'obs_stab'] = test_df['stab_df']\n",
    "    predictions = model.predict(test_seq).reshape(-1)\n",
    "    df.loc[:,output_col] = scaler.inverse_transform(predictions)\n",
    "    return df\n",
    "\n",
    "def one_hot_encode(df, col='utr', seq_len=50):\n",
    "    # Dictionary returning one-hot encoding of nucleotides. \n",
    "    nuc_d = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n",
    "    \n",
    "    # Creat empty matrix.\n",
    "    vectors=np.empty([len(df),seq_len,4])\n",
    "    \n",
    "    # Iterate through UTRs and one-hot encode\n",
    "    for i,seq in enumerate(df[col].str[:seq_len]): \n",
    "        seq = seq.lower()\n",
    "        a = np.array([nuc_d[x] for x in seq])\n",
    "        vectors[i] = a\n",
    "    return vectors\n",
    "\n",
    "def r2(x,y):\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(data[x],data[y])\n",
    "    return r_value**2\n",
    "\n",
    "def eval_data(df, model, test_seq, obs_col, output_col='pred'):\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    scaler.fit(df[obs_col].reshape(-1,1))\n",
    "    #df.loc[:,'obs_stab'] = test_df['stab_df']\n",
    "    predictions = model.predict(test_seq).reshape(-1)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(df[obs_col],scaler.inverse_transform(predictions))\n",
    "    return r_value**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load all EGFP data. Rename total reads and RL columns to represent each data set in the merged dataframe.\n",
    "e1 & e2 = unmodified EGFP........ep1 & ep2 = pseudouridine EGFP........emp1 & emp2 = m1pseudouridine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e1 = pd.read_pickle('../../data/egfp_unmod_1.pkl')\n",
    "e1['e1_rl'] = e1['rl']\n",
    "e1['e1_total'] = e1['total_reads']\n",
    "e1['utr'] = e1['utr'].str[:50]\n",
    "\n",
    "e2 = pd.read_pickle('../../data/egfp_unmod_2.pkl')\n",
    "e2['e2_rl'] = e2['rl']\n",
    "e2['e2_total'] = e2['total']\n",
    "e2['utr'] = e2['utr'].str[:50]\n",
    "\n",
    "ep1 = pd.read_pickle('../../data/egfp_pseudo_1.pkl')\n",
    "ep1['ep1_rl'] = ep1['rl']\n",
    "ep1['ep1_total'] = ep1['total']\n",
    "ep1['utr'] = ep1['utr'].str[:50]\n",
    "\n",
    "ep2 = pd.read_pickle('../../data/egfp_pseudo_2.pkl')\n",
    "ep2['ep2_rl'] = ep2['rl']\n",
    "ep2['ep2_total'] = ep2['total']\n",
    "ep2['utr'] = ep2['utr'].str[:50]\n",
    "\n",
    "emp1 = pd.read_pickle('../../data/egfp_m1pseudo_1.pkl')\n",
    "emp1['emp1_rl'] = emp1['rl']\n",
    "emp1['emp1_total'] = emp1['total']\n",
    "emp1['utr'] = emp1['utr'].str[:50]\n",
    "                      \n",
    "emp2 = pd.read_pickle('../../data/egfp_m1pseudo_2.pkl')\n",
    "emp2['emp2_rl'] = emp2['rl']\n",
    "emp2['emp2_total'] = emp2['total']\n",
    "emp2['utr'] = emp2['utr'].str[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge data into a single dataframe. Take the 'inner' merge to select UTRs that are found in all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "320497\n",
      "320497\n",
      "318921\n",
      "318468\n",
      "318115\n"
     ]
    }
   ],
   "source": [
    "df = pd.merge(e1, e2, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total']]\n",
    "\n",
    "df = pd.merge(df, ep1, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl']]\n",
    "print len(df)\n",
    "df = pd.merge(df, emp1, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl', 'emp1_total', 'emp1_rl']]\n",
    "print len(df)\n",
    "df = pd.merge(df, ep2, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl', 'emp1_total', 'emp1_rl',\n",
    "        'ep2_total', 'ep2_rl']]\n",
    "print len(df)\n",
    "df = pd.merge(df, emp2, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl', 'emp1_total', 'emp1_rl',\n",
    "        'ep2_total', 'ep2_rl', 'emp2_total', 'emp2_rl']]\n",
    "print len(df)\n",
    "\n",
    "df.dropna(how='any', inplace=True)\n",
    "print len(df)\n",
    "\n",
    "e1 = None\n",
    "e2 = None\n",
    "h8 = None\n",
    "h10 = None\n",
    "h8m = None\n",
    "h10m = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Enrich for UTRs that have high read counts across each data set. Sort by reads, remove 10k of lowest UTRs, move on to the next set, sort, cut 10k, etc.\n",
    "Note that the train / test split is the same between all data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:288: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:465: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df.sort_values('e2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:305000]\n",
    "df.sort_values('e1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:300000]\n",
    "df.sort_values('ep1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:290000]\n",
    "df.sort_values('emp1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:280000]\n",
    "df.sort_values('emp2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:270000]\n",
    "df.sort_values('ep2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:260000]\n",
    "\n",
    "df.sort_values('e2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "df['utr'] = df['utr'].str[:50]\n",
    "e_test = df[:20000]\n",
    "e_test.reset_index(inplace=True, drop=True)\n",
    "\n",
    "e_train = df[20000:]\n",
    "e_train.reset_index(inplace=True, drop=True)\n",
    "\n",
    "seq_e_train = one_hot_encode(e_train,seq_len=50)\n",
    "seq_e_test = one_hot_encode(e_test, seq_len=50)\n",
    "\n",
    "e_train.loc[:,'e1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'e1_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'e2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'e2_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'ep1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'ep1_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'ep2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'ep2_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'emp1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'emp1_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'emp2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'emp2_rl'].reshape(-1,1))\n",
    "\n",
    "\n",
    "e_train = e_train[['utr', 'e1_scaled_rl', 'e1_total', 'e2_scaled_rl', 'e2_total', 'ep1_scaled_rl', 'ep1_total',\n",
    "                  'emp1_scaled_rl', 'emp1_total', 'ep2_scaled_rl', 'ep2_total', 'emp2_scaled_rl', 'emp2_total']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load mCherry data and process the same way as the EGFP data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c1 = pd.read_pickle('../../data/mcherry_1.pkl')\n",
    "c2 = pd.read_pickle('../../data/mcherry_2.pkl')\n",
    "c1 = c1.rename(columns={'rl':'c1_rl','total':'c1_total'})\n",
    "c2 = c2.rename(columns={'rl':'c2_rl','total':'c2_total'})\n",
    "mc = pd.merge(c1,c2, on='utr', how='inner')\n",
    "print len(mc)\n",
    "\n",
    "\n",
    "mc.sort_values('c1_total', inplace=True, ascending=False)\n",
    "mc.reset_index(inplace=True, drop=True)\n",
    "mc = mc[:180000]\n",
    "mc.sort_values('c2_total', inplace=True, ascending=False)\n",
    "mc.reset_index(inplace=True, drop=True)\n",
    "mc = mc[:170000]\n",
    "\n",
    "mc.sort_values('c1_total', inplace=True, ascending=False)\n",
    "mc.reset_index(inplace=True, drop=True)\n",
    "\n",
    "c_train = mc[20000:]\n",
    "#c_train.reset_index(inplace=True, drop=True)\n",
    "\n",
    "c_test = mc[:20000]\n",
    "#c_test.reset_index(inplace=True, drop=True)\n",
    "print len(c_test), len(c_train)\n",
    "\n",
    "seq_c_train = binarize_sequences(c_train,seq_len=50)\n",
    "seq_c_test = binarize_sequences(c_test, seq_len=50)\n",
    "\n",
    "c_train.loc[:,'scaled_c1_rl'] = preprocessing.StandardScaler().fit_transform(c_train.loc[:,'c1_rl'].reshape(-1,1))\n",
    "c_train.loc[:,'scaled_c2_rl'] = preprocessing.StandardScaler().fit_transform(c_train.loc[:,'c2_rl'].reshape(-1,1))\n",
    "c_train = c_train[['utr', 'scaled_c1_rl', 'scaled_c2_rl', 'c1_total', 'c2_total']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 101s - loss: 0.2855   \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.1849   \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.1691   \n",
      "e1_model\n",
      "(0.90725775283074139, 0.84681625319453901, 0.69791975004779072, 0.72930242608846052, 0.68696203816548995, 0.75452140569627768)\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 101s - loss: 0.3506   \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 101s - loss: 0.2508   \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.2275   \n",
      "e2_model\n",
      "(0.88364408784072468, 0.88260965714343831, 0.69366278985368368, 0.7502953695399649, 0.67425054818509877, 0.77544874424940169)\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.5313   \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.4423   \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.4089   \n",
      "ep1_model\n",
      "(0.78416780382749829, 0.73274354689593357, 0.76630103511701708, 0.77077188525992713, 0.74855488430891559, 0.74602639947525684)\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.4238   \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.3314   \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.3071   \n",
      "ep2_model\n",
      "(0.80463178212718534, 0.78654258646384301, 0.77587925050419027, 0.82720189997472415, 0.75918089225926688, 0.78807271951517022)\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 101s - loss: 0.5589   \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.4756   \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 100s - loss: 0.4475   \n",
      "emp1_model\n",
      "(0.76776719717997177, 0.72552687188126874, 0.76018818349671591, 0.77277546272580844, 0.75851393841953285, 0.7364997157815788)\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 99s - loss: 0.4742    \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 99s - loss: 0.3579    \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 99s - loss: 0.3324    \n",
      "emp2_model\n",
      "(0.81246035400640815, 0.79409247535453231, 0.73316237209589508, 0.77514172536750425, 0.71044006841487239, 0.80267984116658497)\n"
     ]
    }
   ],
   "source": [
    "model_dict = {}\n",
    "performance_dict = {}\n",
    "\n",
    "for sample in ['e1', 'e2', 'ep1', 'ep2', 'emp1', 'emp2']:\n",
    "    m_name = sample + '_model'\n",
    "\n",
    "    model_dict[m_name] = train_model(seq_e_train, e_train[sample + '_scaled_rl'], nb_epoch=3,border_mode='same',\n",
    "                       inp_len=50, nodes=40, layers=3, nbr_filters=120, filter_len=8, dropout1=0,\n",
    "                       dropout2=0,dropout3=0.2)\n",
    "\n",
    "    model = model_dict[m_name]\n",
    "    e1 = eval_data(e_test, model, seq_e_test, 'e1_rl')\n",
    "    e2 = eval_data(e_test, model, seq_e_test, 'e2_rl')\n",
    "    ep1 = eval_data(e_test, model, seq_e_test, 'ep1_rl')\n",
    "    ep2 = eval_data(e_test, model, seq_e_test, 'ep2_rl')\n",
    "    emp1 = eval_data(e_test, model, seq_e_test, 'emp1_rl')\n",
    "    emp2 = eval_data(e_test, model, seq_e_test, 'emp2_rl')\n",
    "\n",
    "    print m_name\n",
    "    performance_dict[m_name] = (e1, e2, ep1, ep2, emp1, emp2)\n",
    "    print performance_dict[m_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save models and model performance on test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for k, v in model_dict.iteritems():\n",
    "    keras.models.save_model(v, k + '.mdl')\n",
    "    \n",
    "with open('model_performance.dict', 'wb') as f:\n",
    "    pickle.dump(performance_dict, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Only compare EGFP chemistry!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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44oknoNfrAVgeJEg9G/fdd5/siMIDBw6UHcjO+P7MxMREzJo1y2pZJk6caDig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\nJuNERERERERENsZknIiIiIiIiMjG/j+xCmkRRtPUWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5fd7af150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.array((performance_dict['e1_model'], performance_dict['e2_model'], performance_dict['ep1_model'],\n",
    "              performance_dict['ep2_model'], performance_dict['emp1_model'], performance_dict['emp2_model']))\n",
    "\n",
    "ax =sns.heatmap(a, annot=True, cmap='YlOrRd', vmin=0.5, vmax=1, annot_kws={'size':'20', 'color':'black'}, cbar=False, square=True)\n",
    "f = ax.get_figure()\n",
    "f.set_size_inches((9.5,9.5))\n",
    "ax.set_xticklabels([r'U${_E}{_1}$', r'U${_E}{_2}$', r'$\\Psi{_1}$', r'$\\Psi{_2}$', r'$m^1\\Psi{_1}$',\n",
    "                    r'$m^1\\Psi{_2}$'], fontsize=29, fontdict={'family':'arial'})\n",
    "ax.set_yticklabels([r'$m^1\\Psi{_2}$', r'$m^1\\Psi{_1}$', r'$\\Psi{_2}$', r'$\\Psi{_1}$', r'U${_E}{_2}$',\n",
    "                    r'U${_E}{_1}$'], fontsize=29, fontdict={'family':'arial'}, rotation=0)\n",
    "ax.set_xlabel('Model Training Data', size = 35, fontdict={'family':'arial'})\n",
    "ax.set_ylabel('Model Test Data', size=35, fontdict={'family':'arial'})\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.labelpad'] = 5\n",
    "plt.rcParams['axes.linewidth']= 2.5\n",
    "plt.rcParams['xtick.labelsize']= 24\n",
    "plt.rcParams['ytick.labelsize']= 24\n",
    "plt.rcParams['axes.labelsize'] = 20\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "plt.rcParams['lines.linewidth'] = 7\n",
    "plt.rcParams['legend.markerscale'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 20\n",
    "plt.rcParams['xtick.major.size'] = 12\n",
    "plt.rcParams['ytick.major.size'] = 12\n",
    "plt.rcParams['xtick.minor.size'] = 8\n",
    "plt.rcParams['ytick.minor.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 3\n",
    "plt.rcParams['ytick.minor.width'] = 3\n",
    "plt.rcParams['xtick.major.width'] = 4\n",
    "plt.rcParams['ytick.major.width'] = 4\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "plt.rcParams['figure.autolayout'] = True\n",
    "plt.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams['xtick.color'] = 'black'\n",
    "plt.rcParams['ytick.color'] = 'black'\n",
    "plt.rcParams['axes.labelcolor'] = \"black\"\n",
    "plt.rcParams['axes.edgecolor'] = 'black'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.array((d['e1_model'], d['e2_model'], d['c1_model'], d['c2_model'], d['ep1_model'], d['ep2_model'], d['emp1_model'], d['emp2_model']))\n",
    "\n",
    "ax =sns.heatmap(a, annot=True, cmap='YlOrRd', vmin=0.5, vmax=1, annot_kws={'size':'10', 'color':'k'}, cbar=False, square=True)\n",
    "f = ax.get_figure()\n",
    "f.set_size_inches((9.5,9.5))\n",
    "ax.set_xticklabels([r'U${_E}{_1}$', r'U${_E}{_2}$', r'U${_m}{_C}{_1}$', r'U${_m}{_C}{_2}$',r'$\\Psi{_1}$',\n",
    "                    r'$\\Psi{_2}$', r'$m^1\\Psi{_1}$', r'$m^1\\Psi{_2}$'], fontsize=20, fontdict={'family':'arial'})\n",
    "ax.set_yticklabels([r'$m^1\\Psi{_2}$', r'$m^1\\Psi{_1}$', r'$\\Psi{_2}$', r'$\\Psi{_1}$', r'U${_m}{_C}{_2}$', r'U${_m}{_C}{_1}$',\n",
    "                    r'U${_E}{_2}$',  r'U${_E}{_1}$'], fontsize=20, fontdict={'family':'arial'}, rotation=0)\n",
    "ax.set_xlabel('Model Training Data', size = 20, fontdict={'family':'arial'})\n",
    "ax.set_ylabel('Model Test Data', size=20, fontdict={'family':'arial'})\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Previous\n",
    "\n",
    "a = np.array((d['e1_model'], d['e2_model'], d['c1_model'], d['c2_model'], d['ep1_model'], d['ep2_model'], d['emp1_model'], d['emp2_model']))\n",
    "\n",
    "ax =sns.heatmap(a, annot=True, cmap='YlOrRd', vmin=0.5, vmax=1, annot_kws={'size':'10', 'color':'k'}, cbar=False, square=True)\n",
    "f = ax.get_figure()\n",
    "f.set_size_inches((9.5,9.5))\n",
    "ax.set_xticklabels([r'U${_E}{_1}$', r'U${_E}{_2}$', r'U${_m}{_C}{_1}$', r'U${_m}{_C}{_2}$',r'$\\Psi{_1}$',\n",
    "                    r'$\\Psi{_2}$', r'$m^1\\Psi{_1}$', r'$m^1\\Psi{_2}$'], fontsize=20, fontdict={'family':'arial'})\n",
    "ax.set_yticklabels([r'$m^1\\Psi{_2}$', r'$m^1\\Psi{_1}$', r'$\\Psi{_2}$', r'$\\Psi{_1}$', r'U${_m}{_C}{_2}$', r'U${_m}{_C}{_1}$',\n",
    "                    r'U${_E}{_2}$',  r'U${_E}{_1}$'], fontsize=20, fontdict={'family':'arial'}, rotation=0)\n",
    "ax.set_xlabel('Model Training Data', size = 20, fontdict={'family':'arial'})\n",
    "ax.set_ylabel('Model Test Data', size=20, fontdict={'family':'arial'})\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.savefig('/data/5utr/modeling/combined_data/chemistry/images/all_mods_trained_tested_heatmap_20170815.eps', format=('eps'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.savefig('/data/5utr/modeling/combined_data/chemistry/images/egfp_chemistries_only_tested_heatmap_20170925.eps', format=('eps'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
